Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraints

Abstract

There is a history of research focussed on learning of shiftreduce parsers from syntactically annotated corpora by the means of machine learning techniques based on logic. The presence of lexical semantic tags in the treebank has proved useful for learning semantic constraints which limit the amount of nondeterminism in the parsers. The level of generality of the semantic tags used is of direct importance to that task. We combine the ILP system LAPIS with the lexical resource WordNet to learn parsers with semantic constraints. The generality of these constraints is automatically selected by LAPIS from a number of options provided by the corpus annotator. The performance of the parsers learned is evaluated on an original corpus also described in the article.